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RNA Structure01:23

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Overview
The basic structure of RNA consists of a five-carbon sugar and one of four nitrogenous bases. Although most RNA is single-stranded, it can form complex secondary and tertiary structures. Such structures play essential roles in the regulation of transcription and translation.
Different Types of RNA Have the Same Basic Structure
There are three main types of ribonucleic acid (RNA): messenger RNA (mRNA), transfer RNA (tRNA), and ribosomal RNA (rRNA). All three RNA types consist of a...
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The basic structure of RNA consists of a string of ribonucleotides attached by phosphodiester bonds. Although most RNA is single-stranded, it can form complex secondary and tertiary structures. Such structures play essential roles in the regulation of transcription and translation.
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The pentose sugar in DNA is deoxyribose, while in RNA the pentose sugar is ribose. The difference between the sugars is the presence of the hydroxyl group on the ribose's second carbon and a hydrogen on the deoxyribose's second carbon. The phosphate residue attaches to the hydroxyl group of the 5′ carbon of one sugar and the hydroxyl group of the 3′ carbon of the sugar of the next nucleotide, which forms  a 5′ to 3′ phosphodiester linkage.
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RNA Stability01:53

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Intact DNA strands can be found in fossils, while scientists sometimes struggle to keep RNA intact under laboratory conditions. The structural variations between RNA and DNA underlie the differences in their stability and longevity. Because DNA is double-stranded, it is inherently more stable. The single-stranded structure of RNA is less stable but also more flexible and can form weak internal bonds. Additionally, most RNAs in the cell are relatively short, while DNA can be up to 250 million...
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Many proteins’ biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.
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DeepRMSF: un enfoque automatizado basado en aprendizaje profundo para predecir la flexibilidad atómica en la

Chenjie Feng1, Xiaowen Sun2, Xintao Song2

  • 1College of Medical Information and Engineering, Ningxia Medical University, No. 1160 Shengli Road, Xingqing District, Yinchuan, Ningxia Province 750004, China.

Briefings in bioinformatics
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DeepRMSF, un nuevo método de aprendizaje profundo, predice con precisión la flexibilidad vibratoria del ARN a partir de la estructura. Esta herramienta ofrece una alternativa rápida y escalable a las simulaciones de dinámica molecular para analizar la dinámica del ARN.

Palabras clave:
red neuronal convolucional 3Dpredicción de la dinámica del ARNflexibilidad local del ARNsimulación de dinámica molecular

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Área de la Ciencia:

  • Biología Computacional
  • Biología Estructural
  • Bioinformática

Sus antecedentes:

  • Comprender la dinámica conformacional del ARN es crucial para descifrar sus funciones biológicas.
  • Predecir la flexibilidad local del ARN a partir de estructuras estáticas sigue siendo un desafío computacional significativo.
  • Los métodos existentes luchan por predecir eficientemente las propiedades dinámicas del ARN.

Objetivo del estudio:

  • Desarrollar un método basado en aprendizaje profundo, DeepRMSF, para predecir la flexibilidad vibratoria del ARN.
  • Proporcionar una herramienta computacionalmente eficiente para evaluar la dinámica local del ARN.
  • Facilitar el análisis a gran escala de la flexibilidad del ARN en transcriptomas.

Principales métodos:

  • Se desarrolló DeepRMSF, un modelo de aprendizaje profundo que utiliza descripciones a nivel atómico del ARN.
  • Se entrenó el modelo con fluctuaciones de la raíz cuadrada media (RMSF) derivadas de simulaciones de dinámica molecular (MD).
  • Se comparó DeepRMSF en 371 estructuras de ARN no redundantes utilizando validación cruzada rigurosa y un conjunto de prueba independiente.

Principales resultados:

  • DeepRMSF predice con precisión la flexibilidad vibratoria del ARN con una alta correlación (PCC ~0.73-0.75) en conjuntos de prueba independientes.
  • Se logró una aceleración >3000 veces en comparación con las simulaciones de dinámica molecular tradicionales para la predicción de la flexibilidad.
  • Se demostró una sólida precisión de extrapolación para ARN de tamaño mediano (~75 nucleótidos), prediciendo la flexibilidad en ~8.2 segundos.

Conclusiones:

  • DeepRMSF proporciona un enfoque escalable y práctico para el cribado de la flexibilidad del ARN en todo el transcriptoma.
  • El método complementa las simulaciones de MD, ofreciendo una alternativa más rápida para analizar la dinámica del ARN.
  • Facilita una comprensión más profunda de las relaciones estructura-dinámica-función del ARN y ayuda a la biología computacional del ARN.